A high-resolution daily experimental performance evaluation of a large-scale industrial vapor-compression refrigeration system based on real-time IoT data monitoring technology

Abstract

This paper studies a novel comprehensive experimental dynamic performance analysis of a large-scale industrial vapor-compression refrigeration system as a part of a discrete cooling system with the aim of system daily parameter investigation. An innovative high-resolution real-time data monitoring is implemented on the system based on precise internet-of-things sensor technology. For precise evaluation of system performance, energy, exergy, and exergoeconomic aspects were considered to achieve daily results for future energy planning. The results demonstrates that high parameter variations occur during peak hours, while around 70% of both the total refrigeration cooling demand and the total power consumption was during the peak period. Moreover, the system's total exergy destruction rate raised by 88.4% at the peak hours, while the exergy efficiency attained between 27 and 35% throughout the day. In addition, the price of the evaporator outlet air, the desired product of the system, decreased from 96.9 $/GJ at midnight to 61.1 $/GJ at noon peak hours. In this period, the total cost rate for the system increased from 0.89 $/h to 1.10 $/h. The data obtained is not only beneficial regarding dynamic performance analysis of the refrigeration cycle but also can assist for executive purposes regarding energy management and further profit makings.